Overview

Brought to you by YData

Dataset statistics

Number of variables50
Number of observations204
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.8 KiB
Average record size in memory400.6 B

Variable types

Categorical28
Numeric7
Text15

Alerts

ALP is highly overall correlated with GGTHigh correlation
ALT is highly overall correlated with ASTHigh correlation
AST is highly overall correlated with ALT and 1 other fieldsHigh correlation
Alcohol is highly overall correlated with Grams_dayHigh correlation
Encephalopathy is highly overall correlated with HBeAgHigh correlation
GGT is highly overall correlated with ALPHigh correlation
Grams_day is highly overall correlated with AlcoholHigh correlation
HBeAg is highly overall correlated with AST and 2 other fieldsHigh correlation
HIV is highly overall correlated with Packs_yearHigh correlation
PHT is highly overall correlated with Spleno and 1 other fieldsHigh correlation
PS is highly overall correlated with HBeAgHigh correlation
Packs_year is highly overall correlated with HIV and 1 other fieldsHigh correlation
Smoking is highly overall correlated with Packs_yearHigh correlation
Spleno is highly overall correlated with PHT and 1 other fieldsHigh correlation
Varices is highly overall correlated with PHT and 1 other fieldsHigh correlation
HBsAg is highly imbalanced (55.3%) Imbalance
HBeAg is highly imbalanced (88.9%) Imbalance
Cirrhosis is highly imbalanced (52.2%) Imbalance
Endemic is highly imbalanced (67.7%) Imbalance
Obesity is highly imbalanced (50.7%) Imbalance
Hemochro is highly imbalanced (69.7%) Imbalance
HIV is highly imbalanced (88.9%) Imbalance
NASH is highly imbalanced (71.8%) Imbalance
Encephalopathy is highly imbalanced (53.8%) Imbalance
Class is uniformly distributed Uniform
Grams_day has 46 (22.5%) zeros Zeros

Reproduction

Analysis started2025-02-23 06:16:43.808087
Analysis finished2025-02-23 06:16:52.395841
Duration8.59 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Gender
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
162 
0
42 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 162
79.4%
0 42
 
20.6%

Length

2025-02-23T11:46:52.470376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:52.551725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 162
79.4%
0 42
 
20.6%

Most occurring characters

ValueCountFrequency (%)
1 162
79.4%
0 42
 
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 162
79.4%
0 42
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 162
79.4%
0 42
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 162
79.4%
0 42
 
20.6%

Symptoms
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
141 
0
63 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 141
69.1%
0 63
30.9%

Length

2025-02-23T11:46:52.648305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:52.719045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 141
69.1%
0 63
30.9%

Most occurring characters

ValueCountFrequency (%)
1 141
69.1%
0 63
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 141
69.1%
0 63
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 141
69.1%
0 63
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 141
69.1%
0 63
30.9%

Alcohol
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
147 
0
57 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 147
72.1%
0 57
 
27.9%

Length

2025-02-23T11:46:52.803217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:52.866174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 147
72.1%
0 57
 
27.9%

Most occurring characters

ValueCountFrequency (%)
1 147
72.1%
0 57
 
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 147
72.1%
0 57
 
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 147
72.1%
0 57
 
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 147
72.1%
0 57
 
27.9%

HBsAg
Categorical

Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
185 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 185
90.7%
1 19
 
9.3%

Length

2025-02-23T11:46:52.954807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:53.021294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 185
90.7%
1 19
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 185
90.7%
1 19
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 185
90.7%
1 19
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 185
90.7%
1 19
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 185
90.7%
1 19
 
9.3%

HBeAg
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
201 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Length

2025-02-23T11:46:53.096415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:53.149732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

HBcAb
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
151 
1
53 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 151
74.0%
1 53
 
26.0%

Length

2025-02-23T11:46:53.217710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:53.270501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 151
74.0%
1 53
 
26.0%

Most occurring characters

ValueCountFrequency (%)
0 151
74.0%
1 53
 
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 151
74.0%
1 53
 
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 151
74.0%
1 53
 
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 151
74.0%
1 53
 
26.0%

HCVAb
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
162 
1
42 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
79.4%
1 42
 
20.6%

Length

2025-02-23T11:46:53.446489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:53.517383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 162
79.4%
1 42
 
20.6%

Most occurring characters

ValueCountFrequency (%)
0 162
79.4%
1 42
 
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 162
79.4%
1 42
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 162
79.4%
1 42
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 162
79.4%
1 42
 
20.6%

Cirrhosis
Categorical

Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
183 
0
21 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 183
89.7%
0 21
 
10.3%

Length

2025-02-23T11:46:53.602984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:53.669778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 183
89.7%
0 21
 
10.3%

Most occurring characters

ValueCountFrequency (%)
1 183
89.7%
0 21
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 183
89.7%
0 21
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 183
89.7%
0 21
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 183
89.7%
0 21
 
10.3%

Endemic
Categorical

Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
192 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 192
94.1%
1 12
 
5.9%

Length

2025-02-23T11:46:53.747593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:53.818373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 192
94.1%
1 12
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 192
94.1%
1 12
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 192
94.1%
1 12
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 192
94.1%
1 12
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 192
94.1%
1 12
 
5.9%

Smoking
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
112 
1
92 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 112
54.9%
1 92
45.1%

Length

2025-02-23T11:46:53.895843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:53.951566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 112
54.9%
1 92
45.1%

Most occurring characters

ValueCountFrequency (%)
0 112
54.9%
1 92
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112
54.9%
1 92
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112
54.9%
1 92
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112
54.9%
1 92
45.1%

Diabetes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
131 
1
73 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 131
64.2%
1 73
35.8%

Length

2025-02-23T11:46:54.021930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:54.080784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 131
64.2%
1 73
35.8%

Most occurring characters

ValueCountFrequency (%)
0 131
64.2%
1 73
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 131
64.2%
1 73
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 131
64.2%
1 73
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 131
64.2%
1 73
35.8%

Obesity
Categorical

Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
182 
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 182
89.2%
1 22
 
10.8%

Length

2025-02-23T11:46:54.160157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:54.216144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 182
89.2%
1 22
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 182
89.2%
1 22
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 182
89.2%
1 22
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 182
89.2%
1 22
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 182
89.2%
1 22
 
10.8%

Hemochro
Categorical

Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
193 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 193
94.6%
1 11
 
5.4%

Length

2025-02-23T11:46:54.298088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:54.355444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 193
94.6%
1 11
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 193
94.6%
1 11
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 193
94.6%
1 11
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 193
94.6%
1 11
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 193
94.6%
1 11
 
5.4%

AHT
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
129 
1
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 129
63.2%
1 75
36.8%

Length

2025-02-23T11:46:54.443772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:54.566008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 129
63.2%
1 75
36.8%

Most occurring characters

ValueCountFrequency (%)
0 129
63.2%
1 75
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 129
63.2%
1 75
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 129
63.2%
1 75
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 129
63.2%
1 75
36.8%

CRI
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
175 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 175
85.8%
1 29
 
14.2%

Length

2025-02-23T11:46:54.703239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:54.845413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 175
85.8%
1 29
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 175
85.8%
1 29
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175
85.8%
1 29
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175
85.8%
1 29
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175
85.8%
1 29
 
14.2%

HIV
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
201 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Length

2025-02-23T11:46:54.985950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:55.091182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 201
98.5%
1 3
 
1.5%

NASH
Categorical

Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
194 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 194
95.1%
1 10
 
4.9%

Length

2025-02-23T11:46:55.183987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:55.254330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 194
95.1%
1 10
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 194
95.1%
1 10
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 194
95.1%
1 10
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 194
95.1%
1 10
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 194
95.1%
1 10
 
4.9%

Varices
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
129 
0
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 129
63.2%
0 75
36.8%

Length

2025-02-23T11:46:55.337745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:55.402062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 129
63.2%
0 75
36.8%

Most occurring characters

ValueCountFrequency (%)
1 129
63.2%
0 75
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 129
63.2%
0 75
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 129
63.2%
0 75
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 129
63.2%
0 75
36.8%

Spleno
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
109 
0
95 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 109
53.4%
0 95
46.6%

Length

2025-02-23T11:46:55.487860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:55.551266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 109
53.4%
0 95
46.6%

Most occurring characters

ValueCountFrequency (%)
1 109
53.4%
0 95
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 109
53.4%
0 95
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 109
53.4%
0 95
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 109
53.4%
0 95
46.6%

PHT
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
139 
0
65 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 139
68.1%
0 65
31.9%

Length

2025-02-23T11:46:55.623292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:55.679375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 139
68.1%
0 65
31.9%

Most occurring characters

ValueCountFrequency (%)
1 139
68.1%
0 65
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 139
68.1%
0 65
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 139
68.1%
0 65
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 139
68.1%
0 65
31.9%

PVT
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
159 
1
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 159
77.9%
1 45
 
22.1%

Length

2025-02-23T11:46:55.749265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:55.802444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 159
77.9%
1 45
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 159
77.9%
1 45
 
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 159
77.9%
1 45
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 159
77.9%
1 45
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 159
77.9%
1 45
 
22.1%

Metastasis
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
153 
1
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 153
75.0%
1 51
 
25.0%

Length

2025-02-23T11:46:55.877759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:55.932065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 153
75.0%
1 51
 
25.0%

Most occurring characters

ValueCountFrequency (%)
0 153
75.0%
1 51
 
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 153
75.0%
1 51
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 153
75.0%
1 51
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 153
75.0%
1 51
 
25.0%

Hallmark
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
134 
0
70 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 134
65.7%
0 70
34.3%

Length

2025-02-23T11:46:56.005163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:56.059411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 134
65.7%
0 70
34.3%

Most occurring characters

ValueCountFrequency (%)
1 134
65.7%
0 70
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 134
65.7%
0 70
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 134
65.7%
0 70
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 134
65.7%
0 70
34.3%

Age
Real number (ℝ)

Distinct53
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.529412
Minimum20
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-23T11:46:56.147341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile43
Q158.75
median67
Q374.25
95-th percentile82
Maximum93
Range73
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation12.778296
Coefficient of variation (CV)0.19500092
Kurtosis1.0289585
Mean65.529412
Median Absolute Deviation (MAD)8
Skewness-0.84548163
Sum13368
Variance163.28484
MonotonicityNot monotonic
2025-02-23T11:46:56.368226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 10
 
4.9%
62 9
 
4.4%
67 9
 
4.4%
74 9
 
4.4%
64 8
 
3.9%
73 8
 
3.9%
63 7
 
3.4%
78 7
 
3.4%
76 7
 
3.4%
66 7
 
3.4%
Other values (43) 123
60.3%
ValueCountFrequency (%)
20 1
 
0.5%
23 1
 
0.5%
25 1
 
0.5%
27 1
 
0.5%
36 2
1.0%
40 2
1.0%
41 2
1.0%
43 3
1.5%
44 1
 
0.5%
45 2
1.0%
ValueCountFrequency (%)
93 1
 
0.5%
88 1
 
0.5%
87 2
 
1.0%
86 1
 
0.5%
85 1
 
0.5%
84 3
1.5%
83 1
 
0.5%
82 4
2.0%
81 3
1.5%
80 5
2.5%

Grams_day
Real number (ℝ)

High correlation  Zeros 

Distinct41
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.294118
Minimum0
Maximum500
Zeros46
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-23T11:46:56.506277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.25
median85
Q3100
95-th percentile200
Maximum500
Range500
Interquartile range (IQR)80.75

Descriptive statistics

Standard deviation63.181354
Coefficient of variation (CV)0.83912736
Kurtosis9.782102
Mean75.294118
Median Absolute Deviation (MAD)15
Skewness1.8561425
Sum15360
Variance3991.8835
MonotonicityNot monotonic
2025-02-23T11:46:56.626222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
100 70
34.3%
0 46
22.5%
75 10
 
4.9%
200 9
 
4.4%
80 9
 
4.4%
60 8
 
3.9%
70 5
 
2.5%
50 4
 
2.0%
180 3
 
1.5%
86 2
 
1.0%
Other values (31) 38
18.6%
ValueCountFrequency (%)
0 46
22.5%
4 1
 
0.5%
5 2
 
1.0%
12 1
 
0.5%
17 1
 
0.5%
20 2
 
1.0%
23 2
 
1.0%
24 1
 
0.5%
30 1
 
0.5%
35 1
 
0.5%
ValueCountFrequency (%)
500 1
 
0.5%
300 1
 
0.5%
250 1
 
0.5%
200 9
4.4%
180 3
 
1.5%
150 1
 
0.5%
137 1
 
0.5%
128 1
 
0.5%
124 1
 
0.5%
120 2
 
1.0%

Packs_year
Categorical

High correlation 

Distinct46
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
96 
30
11 
67,5
11 
40
 
9
1
 
7
Other values (41)
70 

Length

Max length4
Median length1
Mean length1.5833333
Min length1

Characters and Unicode

Total characters323
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)12.7%

Sample

1st row15
2nd row0
3rd row50
4th row30
5th row30

Common Values

ValueCountFrequency (%)
0 96
47.1%
30 11
 
5.4%
67,5 11
 
5.4%
40 9
 
4.4%
1 7
 
3.4%
60 6
 
2.9%
50 5
 
2.5%
32 3
 
1.5%
20 3
 
1.5%
25 3
 
1.5%
Other values (36) 50
24.5%

Length

2025-02-23T11:46:56.734477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 96
47.1%
30 11
 
5.4%
67,5 11
 
5.4%
40 9
 
4.4%
1 7
 
3.4%
60 6
 
2.9%
50 5
 
2.5%
32 3
 
1.5%
20 3
 
1.5%
25 3
 
1.5%
Other values (36) 50
24.5%

Most occurring characters

ValueCountFrequency (%)
0 136
42.1%
5 30
 
9.3%
3 27
 
8.4%
4 22
 
6.8%
7 22
 
6.8%
1 21
 
6.5%
6 20
 
6.2%
2 19
 
5.9%
, 14
 
4.3%
8 10
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 136
42.1%
5 30
 
9.3%
3 27
 
8.4%
4 22
 
6.8%
7 22
 
6.8%
1 21
 
6.5%
6 20
 
6.2%
2 19
 
5.9%
, 14
 
4.3%
8 10
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 136
42.1%
5 30
 
9.3%
3 27
 
8.4%
4 22
 
6.8%
7 22
 
6.8%
1 21
 
6.5%
6 20
 
6.2%
2 19
 
5.9%
, 14
 
4.3%
8 10
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 136
42.1%
5 30
 
9.3%
3 27
 
8.4%
4 22
 
6.8%
7 22
 
6.8%
1 21
 
6.5%
6 20
 
6.2%
2 19
 
5.9%
, 14
 
4.3%
8 10
 
3.1%

PS
Categorical

High correlation 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
88 
1
44 
2
37 
3
29 
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 88
43.1%
1 44
21.6%
2 37
18.1%
3 29
 
14.2%
4 6
 
2.9%

Length

2025-02-23T11:46:56.818348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:56.898118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 88
43.1%
1 44
21.6%
2 37
18.1%
3 29
 
14.2%
4 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 88
43.1%
1 44
21.6%
2 37
18.1%
3 29
 
14.2%
4 6
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 88
43.1%
1 44
21.6%
2 37
18.1%
3 29
 
14.2%
4 6
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 88
43.1%
1 44
21.6%
2 37
18.1%
3 29
 
14.2%
4 6
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 88
43.1%
1 44
21.6%
2 37
18.1%
3 29
 
14.2%
4 6
 
2.9%

Encephalopathy
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
173 
2
24 
3
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 173
84.8%
2 24
 
11.8%
3 7
 
3.4%

Length

2025-02-23T11:46:56.983045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:57.047805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 173
84.8%
2 24
 
11.8%
3 7
 
3.4%

Most occurring characters

ValueCountFrequency (%)
1 173
84.8%
2 24
 
11.8%
3 7
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 173
84.8%
2 24
 
11.8%
3 7
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 173
84.8%
2 24
 
11.8%
3 7
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 173
84.8%
2 24
 
11.8%
3 7
 
3.4%

Ascites
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
134 
2
47 
3
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 134
65.7%
2 47
 
23.0%
3 23
 
11.3%

Length

2025-02-23T11:46:57.122817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:46:57.181717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 134
65.7%
2 47
 
23.0%
3 23
 
11.3%

Most occurring characters

ValueCountFrequency (%)
1 134
65.7%
2 47
 
23.0%
3 23
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 134
65.7%
2 47
 
23.0%
3 23
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 134
65.7%
2 47
 
23.0%
3 23
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 134
65.7%
2 47
 
23.0%
3 23
 
11.3%

INR
Text

Distinct95
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:46:57.407116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.8872549
Min length1

Characters and Unicode

Total characters793
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)19.1%

Sample

1st row1,53
2nd row1,24
3rd row0,96
4th row0,95
5th row0,94
ValueCountFrequency (%)
1,2 8
 
3.9%
1,33 8
 
3.9%
1,24 6
 
2.9%
1,25 6
 
2.9%
1,46 5
 
2.5%
1,17 5
 
2.5%
1,09 5
 
2.5%
1,3 4
 
2.0%
1,18 4
 
2.0%
1,12 4
 
2.0%
Other values (85) 149
73.0%
2025-02-23T11:46:57.727288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 226
28.5%
, 202
25.5%
2 71
 
9.0%
3 59
 
7.4%
4 52
 
6.6%
5 35
 
4.4%
0 34
 
4.3%
6 32
 
4.0%
9 31
 
3.9%
7 27
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 226
28.5%
, 202
25.5%
2 71
 
9.0%
3 59
 
7.4%
4 52
 
6.6%
5 35
 
4.4%
0 34
 
4.3%
6 32
 
4.0%
9 31
 
3.9%
7 27
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 226
28.5%
, 202
25.5%
2 71
 
9.0%
3 59
 
7.4%
4 52
 
6.6%
5 35
 
4.4%
0 34
 
4.3%
6 32
 
4.0%
9 31
 
3.9%
7 27
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 226
28.5%
, 202
25.5%
2 71
 
9.0%
3 59
 
7.4%
4 52
 
6.6%
5 35
 
4.4%
0 34
 
4.3%
6 32
 
4.0%
9 31
 
3.9%
7 27
 
3.4%

AFP
Text

Distinct170
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:46:57.998945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.4117647
Min length1

Characters and Unicode

Total characters696
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)71.1%

Sample

1st row95
2nd row2,6
3rd row5,8
4th row2440
5th row49
ValueCountFrequency (%)
2,6 4
 
2.0%
41 3
 
1.5%
2,8 3
 
1.5%
3,1 3
 
1.5%
20 3
 
1.5%
18 3
 
1.5%
5,2 3
 
1.5%
2,5 3
 
1.5%
60 2
 
1.0%
3,9 2
 
1.0%
Other values (160) 175
85.8%
2025-02-23T11:46:58.432952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 94
13.5%
2 93
13.4%
1 93
13.4%
5 64
9.2%
4 64
9.2%
7 55
7.9%
6 53
7.6%
3 50
7.2%
8 49
7.0%
9 44
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 94
13.5%
2 93
13.4%
1 93
13.4%
5 64
9.2%
4 64
9.2%
7 55
7.9%
6 53
7.6%
3 50
7.2%
8 49
7.0%
9 44
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 94
13.5%
2 93
13.4%
1 93
13.4%
5 64
9.2%
4 64
9.2%
7 55
7.9%
6 53
7.6%
3 50
7.2%
8 49
7.0%
9 44
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 94
13.5%
2 93
13.4%
1 93
13.4%
5 64
9.2%
4 64
9.2%
7 55
7.9%
6 53
7.6%
3 50
7.2%
8 49
7.0%
9 44
6.3%
Distinct78
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:46:58.618976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7352941
Min length1

Characters and Unicode

Total characters762
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)15.2%

Sample

1st row13,7
2nd row10,3
3rd row8,9
4th row13,4
5th row14,3
ValueCountFrequency (%)
12,6 9
 
4.4%
14,9 8
 
3.9%
12,1 6
 
2.9%
13,1 6
 
2.9%
13 6
 
2.9%
10,8 6
 
2.9%
13,3 5
 
2.5%
11,8 5
 
2.5%
11,3 5
 
2.5%
12,7 5
 
2.5%
Other values (68) 143
70.1%
2025-02-23T11:46:58.953725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 237
31.1%
, 187
24.5%
3 65
 
8.5%
2 50
 
6.6%
4 47
 
6.2%
5 37
 
4.9%
9 34
 
4.5%
6 30
 
3.9%
0 25
 
3.3%
8 25
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 237
31.1%
, 187
24.5%
3 65
 
8.5%
2 50
 
6.6%
4 47
 
6.2%
5 37
 
4.9%
9 34
 
4.5%
6 30
 
3.9%
0 25
 
3.3%
8 25
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 237
31.1%
, 187
24.5%
3 65
 
8.5%
2 50
 
6.6%
4 47
 
6.2%
5 37
 
4.9%
9 34
 
4.5%
6 30
 
3.9%
0 25
 
3.3%
8 25
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 237
31.1%
, 187
24.5%
3 65
 
8.5%
2 50
 
6.6%
4 47
 
6.2%
5 37
 
4.9%
9 34
 
4.5%
6 30
 
3.9%
0 25
 
3.3%
8 25
 
3.3%

MCV
Text

Distinct145
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:46:59.234256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9607843
Min length2

Characters and Unicode

Total characters808
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)49.0%

Sample

1st row106,6
2nd row83
3rd row79,8
4th row97,1
5th row95,1
ValueCountFrequency (%)
95,1 5
 
2.5%
89,5 5
 
2.5%
92 3
 
1.5%
93,8 3
 
1.5%
102 3
 
1.5%
88 3
 
1.5%
92,3 3
 
1.5%
96,1 3
 
1.5%
88,9 3
 
1.5%
91,6 3
 
1.5%
Other values (135) 170
83.3%
2025-02-23T11:46:59.610694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 174
21.5%
9 141
17.5%
1 94
11.6%
8 90
11.1%
0 63
 
7.8%
3 48
 
5.9%
6 46
 
5.7%
2 44
 
5.4%
7 37
 
4.6%
5 36
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 174
21.5%
9 141
17.5%
1 94
11.6%
8 90
11.1%
0 63
 
7.8%
3 48
 
5.9%
6 46
 
5.7%
2 44
 
5.4%
7 37
 
4.6%
5 36
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 174
21.5%
9 141
17.5%
1 94
11.6%
8 90
11.1%
0 63
 
7.8%
3 48
 
5.9%
6 46
 
5.7%
2 44
 
5.4%
7 37
 
4.6%
5 36
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 174
21.5%
9 141
17.5%
1 94
11.6%
8 90
11.1%
0 63
 
7.8%
3 48
 
5.9%
6 46
 
5.7%
2 44
 
5.4%
7 37
 
4.6%
5 36
 
4.5%
Distinct123
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:46:59.853506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.3382353
Min length1

Characters and Unicode

Total characters681
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)37.3%

Sample

1st row4,9
2nd row6,1
3rd row8,4
4th row9
5th row6,4
ValueCountFrequency (%)
5 7
 
3.4%
5,5 6
 
2.9%
4,9 6
 
2.9%
6,1 5
 
2.5%
5,4 5
 
2.5%
4,3 4
 
2.0%
4,1 4
 
2.0%
6,4 3
 
1.5%
9 3
 
1.5%
5,7 3
 
1.5%
Other values (113) 158
77.5%
2025-02-23T11:47:00.189737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 145
21.3%
0 87
12.8%
5 66
9.7%
6 62
9.1%
4 59
8.7%
1 53
 
7.8%
3 52
 
7.6%
9 51
 
7.5%
2 39
 
5.7%
8 38
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 145
21.3%
0 87
12.8%
5 66
9.7%
6 62
9.1%
4 59
8.7%
1 53
 
7.8%
3 52
 
7.6%
9 51
 
7.5%
2 39
 
5.7%
8 38
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 145
21.3%
0 87
12.8%
5 66
9.7%
6 62
9.1%
4 59
8.7%
1 53
 
7.8%
3 52
 
7.6%
9 51
 
7.5%
2 39
 
5.7%
8 38
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 145
21.3%
0 87
12.8%
5 66
9.7%
6 62
9.1%
4 59
8.7%
1 53
 
7.8%
3 52
 
7.6%
9 51
 
7.5%
2 39
 
5.7%
8 38
 
5.6%
Distinct169
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:00.450541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.3676471
Min length2

Characters and Unicode

Total characters1095
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)69.6%

Sample

1st row99
2nd row1,71
3rd row472
4th row279
5th row199
ValueCountFrequency (%)
77000 5
 
2.5%
109000 3
 
1.5%
154000 3
 
1.5%
91000 3
 
1.5%
99 3
 
1.5%
96000 3
 
1.5%
42000 2
 
1.0%
194000 2
 
1.0%
1,71 2
 
1.0%
160000 2
 
1.0%
Other values (159) 176
86.3%
2025-02-23T11:47:00.815416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 410
37.4%
1 136
 
12.4%
7 86
 
7.9%
2 80
 
7.3%
9 64
 
5.8%
8 64
 
5.8%
3 57
 
5.2%
5 54
 
4.9%
4 52
 
4.7%
6 52
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 410
37.4%
1 136
 
12.4%
7 86
 
7.9%
2 80
 
7.3%
9 64
 
5.8%
8 64
 
5.8%
3 57
 
5.2%
5 54
 
4.9%
4 52
 
4.7%
6 52
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 410
37.4%
1 136
 
12.4%
7 86
 
7.9%
2 80
 
7.3%
9 64
 
5.8%
8 64
 
5.8%
3 57
 
5.2%
5 54
 
4.9%
4 52
 
4.7%
6 52
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 410
37.4%
1 136
 
12.4%
7 86
 
7.9%
2 80
 
7.3%
9 64
 
5.8%
8 64
 
5.8%
3 57
 
5.2%
5 54
 
4.9%
4 52
 
4.7%
6 52
 
4.7%
Distinct68
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:00.997661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1078431
Min length1

Characters and Unicode

Total characters634
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)17.2%

Sample

1st row3,4
2nd row3,9
3rd row3,3
4th row3,7
5th row4,1
ValueCountFrequency (%)
3,1 13
 
6.4%
4,2 12
 
5.9%
3,2 12
 
5.9%
3,4 9
 
4.4%
3,6 9
 
4.4%
3,5 9
 
4.4%
4,1 9
 
4.4%
3,8 8
 
3.9%
2,7 7
 
3.4%
3 7
 
3.4%
Other values (58) 109
53.4%
2025-02-23T11:47:01.261547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 192
30.3%
3 132
20.8%
4 87
13.7%
2 78
12.3%
1 38
 
6.0%
6 25
 
3.9%
8 22
 
3.5%
5 21
 
3.3%
7 21
 
3.3%
9 18
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 192
30.3%
3 132
20.8%
4 87
13.7%
2 78
12.3%
1 38
 
6.0%
6 25
 
3.9%
8 22
 
3.5%
5 21
 
3.3%
7 21
 
3.3%
9 18
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 192
30.3%
3 132
20.8%
4 87
13.7%
2 78
12.3%
1 38
 
6.0%
6 25
 
3.9%
8 22
 
3.5%
5 21
 
3.3%
7 21
 
3.3%
9 18
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 192
30.3%
3 132
20.8%
4 87
13.7%
2 78
12.3%
1 38
 
6.0%
6 25
 
3.9%
8 22
 
3.5%
5 21
 
3.3%
7 21
 
3.3%
9 18
 
2.8%
Distinct95
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:01.518500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0833333
Min length1

Characters and Unicode

Total characters629
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)31.4%

Sample

1st row2,1
2nd row0,8
3rd row0,4
4th row0,4
5th row0,7
ValueCountFrequency (%)
0,8 12
 
5.9%
0,5 12
 
5.9%
1,3 11
 
5.4%
1 11
 
5.4%
0,7 10
 
4.9%
0,9 8
 
3.9%
1,4 7
 
3.4%
0,6 6
 
2.9%
1,7 5
 
2.5%
1,2 5
 
2.5%
Other values (85) 117
57.4%
2025-02-23T11:47:01.926150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 189
30.0%
1 92
14.6%
0 66
 
10.5%
3 49
 
7.8%
2 48
 
7.6%
8 34
 
5.4%
5 34
 
5.4%
7 32
 
5.1%
6 29
 
4.6%
4 28
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 189
30.0%
1 92
14.6%
0 66
 
10.5%
3 49
 
7.8%
2 48
 
7.6%
8 34
 
5.4%
5 34
 
5.4%
7 32
 
5.1%
6 29
 
4.6%
4 28
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 189
30.0%
1 92
14.6%
0 66
 
10.5%
3 49
 
7.8%
2 48
 
7.6%
8 34
 
5.4%
5 34
 
5.4%
7 32
 
5.1%
6 29
 
4.6%
4 28
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 189
30.0%
1 92
14.6%
0 66
 
10.5%
3 49
 
7.8%
2 48
 
7.6%
8 34
 
5.4%
5 34
 
5.4%
7 32
 
5.1%
6 29
 
4.6%
4 28
 
4.5%

ALT
Real number (ℝ)

High correlation 

Distinct104
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.372549
Minimum11
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:02.047043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile18.15
Q131
median53
Q379
95-th percentile153.4
Maximum420
Range409
Interquartile range (IQR)48

Descriptive statistics

Standard deviation52.97149
Coefficient of variation (CV)0.79809335
Kurtosis11.512018
Mean66.372549
Median Absolute Deviation (MAD)23
Skewness2.6832743
Sum13540
Variance2805.9788
MonotonicityNot monotonic
2025-02-23T11:47:02.316926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 7
 
3.4%
43 6
 
2.9%
28 6
 
2.9%
35 6
 
2.9%
42 6
 
2.9%
62 5
 
2.5%
27 5
 
2.5%
26 5
 
2.5%
34 4
 
2.0%
70 4
 
2.0%
Other values (94) 150
73.5%
ValueCountFrequency (%)
11 3
1.5%
13 1
 
0.5%
15 1
 
0.5%
16 2
1.0%
17 1
 
0.5%
18 3
1.5%
19 2
1.0%
20 2
1.0%
21 1
 
0.5%
22 1
 
0.5%
ValueCountFrequency (%)
420 1
0.5%
299 1
0.5%
262 1
0.5%
217 1
0.5%
207 1
0.5%
204 1
0.5%
195 1
0.5%
178 1
0.5%
164 1
0.5%
162 1
0.5%

AST
Real number (ℝ)

High correlation 

Distinct122
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.02451
Minimum17
Maximum553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:02.481326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile28.15
Q148
median73.5
Q3111.25
95-th percentile250.8
Maximum553
Range536
Interquartile range (IQR)63.25

Descriptive statistics

Standard deviation81.639327
Coefficient of variation (CV)0.85019259
Kurtosis9.5581633
Mean96.02451
Median Absolute Deviation (MAD)28.5
Skewness2.729893
Sum19589
Variance6664.9797
MonotonicityNot monotonic
2025-02-23T11:47:02.647408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 6
 
2.9%
29 5
 
2.5%
86 4
 
2.0%
52 4
 
2.0%
91 4
 
2.0%
38 4
 
2.0%
32 4
 
2.0%
63 4
 
2.0%
80 4
 
2.0%
67 3
 
1.5%
Other values (112) 162
79.4%
ValueCountFrequency (%)
17 2
 
1.0%
19 1
 
0.5%
20 1
 
0.5%
23 1
 
0.5%
24 1
 
0.5%
26 2
 
1.0%
27 1
 
0.5%
28 2
 
1.0%
29 5
2.5%
30 1
 
0.5%
ValueCountFrequency (%)
553 1
0.5%
523 1
0.5%
401 1
0.5%
357 1
0.5%
354 1
0.5%
335 1
0.5%
334 1
0.5%
325 1
0.5%
306 1
0.5%
266 1
0.5%

GGT
Real number (ℝ)

High correlation 

Distinct166
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.44118
Minimum23
Maximum1575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:02.809748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile45.15
Q193.5
median184.5
Q3352.25
95-th percentile786.6
Maximum1575
Range1552
Interquartile range (IQR)258.75

Descriptive statistics

Standard deviation251.68148
Coefficient of variation (CV)0.9272045
Kurtosis5.1889593
Mean271.44118
Median Absolute Deviation (MAD)104.5
Skewness1.9987548
Sum55374
Variance63343.568
MonotonicityNot monotonic
2025-02-23T11:47:02.999048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 4
 
2.0%
80 4
 
2.0%
23 3
 
1.5%
115 3
 
1.5%
196 3
 
1.5%
143 2
 
1.0%
184 2
 
1.0%
205 2
 
1.0%
75 2
 
1.0%
38 2
 
1.0%
Other values (156) 177
86.8%
ValueCountFrequency (%)
23 3
1.5%
33 1
 
0.5%
34 1
 
0.5%
35 1
 
0.5%
38 2
1.0%
40 1
 
0.5%
44 1
 
0.5%
45 1
 
0.5%
46 1
 
0.5%
49 1
 
0.5%
ValueCountFrequency (%)
1575 1
0.5%
1390 1
0.5%
1020 1
0.5%
993 1
0.5%
983 1
0.5%
924 1
0.5%
879 1
0.5%
869 1
0.5%
833 1
0.5%
816 1
0.5%

ALP
Real number (ℝ)

High correlation 

Distinct148
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.17647
Minimum1
Maximum980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:03.158096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70.45
Q1109.75
median168
Q3277.25
95-th percentile578.4
Maximum980
Range979
Interquartile range (IQR)167.5

Descriptive statistics

Standard deviation168.85257
Coefficient of variation (CV)0.76342917
Kurtosis5.832859
Mean221.17647
Median Absolute Deviation (MAD)67
Skewness2.2004961
Sum45120
Variance28511.19
MonotonicityNot monotonic
2025-02-23T11:47:03.303627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 6
 
2.9%
120 5
 
2.5%
109 5
 
2.5%
141 4
 
2.0%
108 3
 
1.5%
166 3
 
1.5%
117 3
 
1.5%
85 3
 
1.5%
113 3
 
1.5%
174 3
 
1.5%
Other values (138) 166
81.4%
ValueCountFrequency (%)
1 1
0.5%
44 1
0.5%
55 1
0.5%
56 1
0.5%
62 1
0.5%
63 1
0.5%
66 1
0.5%
68 2
1.0%
70 2
1.0%
73 1
0.5%
ValueCountFrequency (%)
980 1
0.5%
974 1
0.5%
923 1
0.5%
913 1
0.5%
684 1
0.5%
670 1
0.5%
649 1
0.5%
629 1
0.5%
595 1
0.5%
587 1
0.5%

TP
Text

Distinct51
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:03.520865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7892157
Min length1

Characters and Unicode

Total characters569
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)9.3%

Sample

1st row7,1
2nd row7
3rd row7
4th row8,1
5th row6,9
ValueCountFrequency (%)
7,2 14
 
6.9%
7 13
 
6.4%
7,3 12
 
5.9%
6,3 10
 
4.9%
7,1 10
 
4.9%
6,7 9
 
4.4%
6,8 9
 
4.4%
6,9 8
 
3.9%
7,6 8
 
3.9%
6,1 7
 
3.4%
Other values (41) 104
51.0%
2025-02-23T11:47:03.853613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 177
31.1%
7 104
18.3%
6 86
15.1%
5 44
 
7.7%
8 38
 
6.7%
3 28
 
4.9%
2 26
 
4.6%
1 23
 
4.0%
9 21
 
3.7%
4 21
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 177
31.1%
7 104
18.3%
6 86
15.1%
5 44
 
7.7%
8 38
 
6.7%
3 28
 
4.9%
2 26
 
4.6%
1 23
 
4.0%
9 21
 
3.7%
4 21
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 177
31.1%
7 104
18.3%
6 86
15.1%
5 44
 
7.7%
8 38
 
6.7%
3 28
 
4.9%
2 26
 
4.6%
1 23
 
4.0%
9 21
 
3.7%
4 21
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 177
31.1%
7 104
18.3%
6 86
15.1%
5 44
 
7.7%
8 38
 
6.7%
3 28
 
4.9%
2 26
 
4.6%
1 23
 
4.0%
9 21
 
3.7%
4 21
 
3.7%
Distinct95
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:04.101396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6421569
Min length1

Characters and Unicode

Total characters743
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)24.0%

Sample

1st row0,7
2nd row0,58
3rd row2,1
4th row1,11
5th row1,8
ValueCountFrequency (%)
0,7 12
 
5.9%
0,9 11
 
5.4%
0,8 11
 
5.4%
1,1 6
 
2.9%
0,77 6
 
2.9%
0,88 4
 
2.0%
0,79 4
 
2.0%
0,71 4
 
2.0%
0,82 4
 
2.0%
1 4
 
2.0%
Other values (85) 138
67.6%
2025-02-23T11:47:04.432491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 200
26.9%
0 145
19.5%
1 87
11.7%
7 61
 
8.2%
8 61
 
8.2%
9 41
 
5.5%
2 39
 
5.2%
6 37
 
5.0%
5 28
 
3.8%
3 25
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 200
26.9%
0 145
19.5%
1 87
11.7%
7 61
 
8.2%
8 61
 
8.2%
9 41
 
5.5%
2 39
 
5.2%
6 37
 
5.0%
5 28
 
3.8%
3 25
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 200
26.9%
0 145
19.5%
1 87
11.7%
7 61
 
8.2%
8 61
 
8.2%
9 41
 
5.5%
2 39
 
5.2%
6 37
 
5.0%
5 28
 
3.8%
3 25
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 200
26.9%
0 145
19.5%
1 87
11.7%
7 61
 
8.2%
8 61
 
8.2%
9 41
 
5.5%
2 39
 
5.2%
6 37
 
5.0%
5 28
 
3.8%
3 25
 
3.4%

Nodule
Real number (ℝ)

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7352941
Minimum0
Maximum5
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:04.513676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7358944
Coefficient of variation (CV)0.63462807
Kurtosis-1.6325954
Mean2.7352941
Median Absolute Deviation (MAD)1
Skewness0.32293624
Sum558
Variance3.0133295
MonotonicityNot monotonic
2025-02-23T11:47:04.611248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 78
38.2%
5 66
32.4%
2 34
16.7%
3 18
 
8.8%
4 7
 
3.4%
0 1
 
0.5%
ValueCountFrequency (%)
0 1
 
0.5%
1 78
38.2%
2 34
16.7%
3 18
 
8.8%
4 7
 
3.4%
5 66
32.4%
ValueCountFrequency (%)
5 66
32.4%
4 7
 
3.4%
3 18
 
8.8%
2 34
16.7%
1 78
38.2%
0 1
 
0.5%
Distinct84
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:04.911372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.4558824
Min length1

Characters and Unicode

Total characters501
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)20.6%

Sample

1st row3,5
2nd row1,8
3rd row13
4th row15,7
5th row9
ValueCountFrequency (%)
3,5 10
 
4.9%
2 10
 
4.9%
3 9
 
4.4%
4 7
 
3.4%
6 7
 
3.4%
9 6
 
2.9%
2,3 6
 
2.9%
4,5 5
 
2.5%
15 5
 
2.5%
10 5
 
2.5%
Other values (74) 134
65.7%
2025-02-23T11:47:05.234634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 129
25.7%
2 56
11.2%
3 55
11.0%
5 55
11.0%
1 51
 
10.2%
4 35
 
7.0%
8 31
 
6.2%
6 30
 
6.0%
7 26
 
5.2%
9 22
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 129
25.7%
2 56
11.2%
3 55
11.0%
5 55
11.0%
1 51
 
10.2%
4 35
 
7.0%
8 31
 
6.2%
6 30
 
6.0%
7 26
 
5.2%
9 22
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 129
25.7%
2 56
11.2%
3 55
11.0%
5 55
11.0%
1 51
 
10.2%
4 35
 
7.0%
8 31
 
6.2%
6 30
 
6.0%
7 26
 
5.2%
9 22
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 129
25.7%
2 56
11.2%
3 55
11.0%
5 55
11.0%
1 51
 
10.2%
4 35
 
7.0%
8 31
 
6.2%
6 30
 
6.0%
7 26
 
5.2%
9 22
 
4.4%
Distinct73
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:05.409149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1666667
Min length1

Characters and Unicode

Total characters646
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)23.0%

Sample

1st row0,5
2nd row0,85
3rd row0,1
4th row0,2
5th row0,1
ValueCountFrequency (%)
0,3 26
 
12.7%
0,5 16
 
7.8%
0,2 14
 
6.9%
0,4 11
 
5.4%
0,7 10
 
4.9%
1,2 8
 
3.9%
1,1 8
 
3.9%
1 7
 
3.4%
1,9 7
 
3.4%
0,6 6
 
2.9%
Other values (63) 91
44.6%
2025-02-23T11:47:05.721059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 197
30.5%
0 121
18.7%
1 85
13.2%
3 51
 
7.9%
2 46
 
7.1%
5 39
 
6.0%
7 23
 
3.6%
9 23
 
3.6%
4 21
 
3.3%
6 21
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 646
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 197
30.5%
0 121
18.7%
1 85
13.2%
3 51
 
7.9%
2 46
 
7.1%
5 39
 
6.0%
7 23
 
3.6%
9 23
 
3.6%
4 21
 
3.3%
6 21
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 646
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 197
30.5%
0 121
18.7%
1 85
13.2%
3 51
 
7.9%
2 46
 
7.1%
5 39
 
6.0%
7 23
 
3.6%
9 23
 
3.6%
4 21
 
3.3%
6 21
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 646
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 197
30.5%
0 121
18.7%
1 85
13.2%
3 51
 
7.9%
2 46
 
7.1%
5 39
 
6.0%
7 23
 
3.6%
9 23
 
3.6%
4 21
 
3.3%
6 21
 
3.3%

Iron
Text

Distinct105
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:05.923482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.7254902
Min length1

Characters and Unicode

Total characters556
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)31.4%

Sample

1st row52,5
2nd row32
3rd row28
4th row131
5th row59
ValueCountFrequency (%)
94 15
 
7.4%
224 8
 
3.9%
25 8
 
3.9%
52,5 7
 
3.4%
37 5
 
2.5%
91 5
 
2.5%
131 4
 
2.0%
85 4
 
2.0%
53 4
 
2.0%
184 4
 
2.0%
Other values (95) 140
68.6%
2025-02-23T11:47:06.343656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 90
16.2%
2 71
12.8%
5 68
12.2%
4 59
10.6%
9 49
8.8%
, 46
8.3%
8 42
7.6%
7 37
6.7%
3 33
 
5.9%
6 32
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 90
16.2%
2 71
12.8%
5 68
12.2%
4 59
10.6%
9 49
8.8%
, 46
8.3%
8 42
7.6%
7 37
6.7%
3 33
 
5.9%
6 32
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 90
16.2%
2 71
12.8%
5 68
12.2%
4 59
10.6%
9 49
8.8%
, 46
8.3%
8 42
7.6%
7 37
6.7%
3 33
 
5.9%
6 32
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 90
16.2%
2 71
12.8%
5 68
12.2%
4 59
10.6%
9 49
8.8%
, 46
8.3%
8 42
7.6%
7 37
6.7%
3 33
 
5.9%
6 32
 
5.8%

Sat
Text

Distinct70
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:06.513273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.9558824
Min length1

Characters and Unicode

Total characters399
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)11.8%

Sample

1st row37
2nd row10
3rd row6
4th row78
5th row15
ValueCountFrequency (%)
37 15
 
7.4%
27 14
 
6.9%
95 9
 
4.4%
13 9
 
4.4%
73 7
 
3.4%
25 6
 
2.9%
18 6
 
2.9%
31 6
 
2.9%
17 6
 
2.9%
26 5
 
2.5%
Other values (60) 121
59.3%
2025-02-23T11:47:06.806631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 62
15.5%
3 61
15.3%
1 57
14.3%
2 50
12.5%
9 38
9.5%
5 37
9.3%
6 33
8.3%
8 28
7.0%
4 16
 
4.0%
0 13
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 62
15.5%
3 61
15.3%
1 57
14.3%
2 50
12.5%
9 38
9.5%
5 37
9.3%
6 33
8.3%
8 28
7.0%
4 16
 
4.0%
0 13
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 62
15.5%
3 61
15.3%
1 57
14.3%
2 50
12.5%
9 38
9.5%
5 37
9.3%
6 33
8.3%
8 28
7.0%
4 16
 
4.0%
0 13
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 62
15.5%
3 61
15.3%
1 57
14.3%
2 50
12.5%
9 38
9.5%
5 37
9.3%
6 33
8.3%
8 28
7.0%
4 16
 
4.0%
0 13
 
3.3%
Distinct117
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-02-23T11:47:07.040802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8235294
Min length1

Characters and Unicode

Total characters576
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)37.7%

Sample

1st row856
2nd row18
3rd row16
4th row1316
5th row22
ValueCountFrequency (%)
70 8
 
3.9%
363 8
 
3.9%
60 8
 
3.9%
856 7
 
3.4%
80 6
 
2.9%
48 6
 
2.9%
982 4
 
2.0%
173 4
 
2.0%
76,9 4
 
2.0%
1316 4
 
2.0%
Other values (107) 145
71.1%
2025-02-23T11:47:07.379286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 74
12.8%
6 68
11.8%
0 65
11.3%
2 62
10.8%
8 62
10.8%
3 59
10.2%
7 57
9.9%
4 47
8.2%
9 41
7.1%
5 36
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 74
12.8%
6 68
11.8%
0 65
11.3%
2 62
10.8%
8 62
10.8%
3 59
10.2%
7 57
9.9%
4 47
8.2%
9 41
7.1%
5 36
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 74
12.8%
6 68
11.8%
0 65
11.3%
2 62
10.8%
8 62
10.8%
3 59
10.2%
7 57
9.9%
4 47
8.2%
9 41
7.1%
5 36
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 74
12.8%
6 68
11.8%
0 65
11.3%
2 62
10.8%
8 62
10.8%
3 59
10.2%
7 57
9.9%
4 47
8.2%
9 41
7.1%
5 36
6.2%

Class
Categorical

Uniform 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
102 
0
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters204
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 102
50.0%
0 102
50.0%

Length

2025-02-23T11:47:07.524613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T11:47:07.619291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 102
50.0%
0 102
50.0%

Most occurring characters

ValueCountFrequency (%)
1 102
50.0%
0 102
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 102
50.0%
0 102
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 102
50.0%
0 102
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 102
50.0%
0 102
50.0%

Interactions

2025-02-23T11:46:50.977990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:47.355413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.036494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.583404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.167064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.797972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.374525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:51.064151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:47.463340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.117201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.668369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.243002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.882847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.457816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:51.142901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:47.605559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.190601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.750728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.316400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.965153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.543394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:51.224695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:47.706654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.271178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.835396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.392726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.052228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.633505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:51.300983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:47.789792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.346297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.917012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.549233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.132711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.716336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:51.383351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:47.880682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.430468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.006613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.646915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.218302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.807515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:51.551615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:47.956628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:48.504166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.083957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:49.726514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.293441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T11:46:50.891993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-23T11:47:07.796990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AHTALPALTASTAgeAlcoholAscitesCRICirrhosisClassDiabetesEncephalopathyEndemicGGTGenderGrams_dayHBcAbHBeAgHBsAgHCVAbHIVHallmarkHemochroMetastasisNASHNoduleObesityPHTPSPVTPacks_yearSmokingSplenoSymptomsVarices
AHT1.0000.1310.1680.0000.3400.0000.0000.2480.1810.0000.3250.1440.0000.0920.1030.1140.0920.0000.0000.0700.0000.0000.1440.0530.0000.0000.0360.0350.1240.0250.2890.0000.2260.1290.128
ALP0.1311.0000.2760.290-0.0380.1940.1510.0000.3810.3610.1670.2750.0000.6010.2380.0260.0000.4760.1860.1120.0000.1960.0000.1250.0000.1240.0000.2000.2630.1520.0590.2520.1320.1190.244
ALT0.1680.2761.0000.731-0.2640.0610.0000.0000.1610.0360.1510.0000.2400.3900.025-0.0730.2690.2880.1640.3980.0000.0000.0000.0000.2770.1360.1300.0000.0540.0480.0000.1140.0000.1380.000
AST0.0000.2900.7311.000-0.1570.0000.1200.0000.0000.2750.0700.2760.0000.3680.0000.0630.1260.6530.1980.3460.0000.1250.0000.2100.0000.1840.0000.0000.2180.1080.2440.0000.0000.1430.055
Age0.340-0.038-0.264-0.1571.0000.2040.0000.3070.2680.1610.2550.1540.2180.0030.3040.0280.2170.1740.2740.3000.2240.0000.0000.0000.000-0.0770.0850.1910.0000.0000.0000.1320.1870.2510.196
Alcohol0.0000.1940.0610.0000.2041.0000.2810.0260.4500.0000.0000.1670.0710.0000.4210.8620.0000.0000.0000.1690.0000.2420.1030.0000.0000.0000.0620.4010.2090.0810.2320.1170.0830.0000.181
Ascites0.0000.1510.0000.1200.0000.2811.0000.0000.1870.2160.1480.2970.0000.0000.1330.2880.1330.2040.1810.0000.0000.0000.0000.0990.0000.0970.0000.4630.3070.1510.1760.0000.2730.1710.280
CRI0.2480.0000.0000.0000.3070.0260.0001.0000.1970.0880.2280.0000.0160.0000.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.1060.1120.3130.0000.0880.0000.000
Cirrhosis0.1810.3810.1610.0000.2680.4500.1870.1971.0000.0000.0000.0190.0510.1330.1950.3940.1280.0000.0400.0000.0000.0340.0000.0000.0000.1830.0590.3680.1160.1000.2190.0000.2740.0770.355
Class0.0000.3610.0360.2750.1610.0000.2160.0880.0001.0000.1010.1660.1280.1580.0000.0000.0000.0410.0000.0000.0000.0170.0000.2400.0000.0940.0000.0000.3960.1500.2300.0830.0000.2670.000
Diabetes0.3250.1670.1510.0700.2550.0000.1480.2280.0000.1011.0000.0890.0000.0000.0000.0000.2830.0000.1730.0000.0000.1470.0000.0000.0000.0000.0000.0000.2030.1190.0000.0000.0000.1120.000
Encephalopathy0.1440.2750.0000.2760.1540.1670.2970.0000.0190.1660.0891.0000.0370.1290.0220.0990.2390.6420.4880.0570.0000.2110.1570.0000.0000.1690.0000.2420.4440.1440.1790.0000.1820.0790.124
Endemic0.0000.0000.2400.0000.2180.0710.0000.0160.0510.1280.0000.0371.0000.3440.0000.2080.1450.0000.2330.0780.0000.0110.0000.0000.0000.1040.0000.1310.0660.0610.0000.0000.0000.0000.044
GGT0.0920.6010.3900.3680.0030.0000.0000.0000.1330.1580.0000.1290.3441.0000.0000.0050.0000.0000.0600.2040.0000.0000.1660.0920.2540.1160.0900.0420.0500.1480.2270.0000.1170.0000.122
Gender0.1030.2380.0250.0000.3040.4210.1330.0000.1950.0000.0000.0220.0000.0001.0000.4200.0000.0000.1240.0000.0000.0000.0640.0000.0000.1230.0000.2270.1030.0000.1790.2960.1260.0000.074
Grams_day0.1140.026-0.0730.0630.0280.8620.2880.0980.3940.0000.0000.0990.2080.0050.4201.0000.0000.1560.0940.1750.0000.1810.1890.1020.000-0.0090.1270.4000.1530.1140.4100.1690.0960.0000.148
HBcAb0.0920.0000.2690.1260.2170.0000.1330.0000.1280.0000.2830.2390.1450.0000.0000.0001.0000.1440.4790.3130.1440.0000.0000.0000.0000.0000.0000.1090.1270.0000.2600.0760.0130.0950.000
HBeAg0.0000.4760.2880.6530.1740.0000.2040.0000.0000.0410.0000.6420.0000.0000.0000.1560.1441.0000.3040.0550.0000.1050.0000.0000.0000.0000.0000.0000.6890.0000.0000.0000.0210.0000.000
HBsAg0.0000.1860.1640.1980.2740.0000.1810.0000.0400.0000.1730.4880.2330.0600.1240.0940.4790.3041.0000.0000.0000.0000.0000.1280.0000.1280.0470.0600.2250.0000.2030.0000.0000.0000.000
HCVAb0.0700.1120.3980.3460.3000.1690.0000.0000.0000.0000.0000.0570.0780.2040.0000.1750.3130.0550.0001.0000.1770.0000.0000.0000.0000.1960.0370.0000.1510.1210.3140.0860.0250.0000.000
HIV0.0000.0000.0000.0000.2240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1440.0000.0000.1771.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5510.0620.0000.1200.000
Hallmark0.0000.1960.0000.1250.0000.2420.0000.0000.0340.0170.1470.2110.0110.0000.0000.1810.0000.1050.0000.0000.0001.0000.0000.0000.0000.1540.0000.2150.2470.0000.2880.0000.1000.0000.000
Hemochro0.1440.0000.0000.0000.0000.1030.0000.0000.0000.0000.0000.1570.0000.1660.0640.1890.0000.0000.0000.0000.0000.0001.0000.0880.0000.1590.0000.0000.1190.0000.3310.0000.0000.0000.000
Metastasis0.0530.1250.0000.2100.0000.0000.0990.0000.0000.2400.0000.0000.0000.0920.0000.1020.0000.0000.1280.0000.0000.0000.0881.0000.0780.4390.0000.0360.3480.0000.1490.1300.0000.2160.000
NASH0.0000.0000.2770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0781.0000.0000.2410.0000.0000.0000.0000.0000.1090.0000.217
Nodule0.0000.1240.1360.184-0.0770.0000.0970.0000.1830.0940.0000.1690.1040.1160.123-0.0090.0000.0000.1280.1960.0000.1540.1590.4390.0001.0000.0200.0000.0000.0000.4080.1000.0000.0930.000
Obesity0.0360.0000.1300.0000.0850.0620.0000.0000.0590.0000.0000.0000.0000.0900.0000.1270.0000.0000.0470.0370.0000.0000.0000.0000.2410.0201.0000.0480.1420.0000.1390.0420.0000.1060.000
PHT0.0350.2000.0000.0000.1910.4010.4630.1080.3680.0000.0000.2420.1310.0420.2270.4000.1090.0000.0600.0000.0000.2150.0000.0360.0000.0000.0481.0000.3130.1590.2700.1260.5500.0000.600
PS0.1240.2630.0540.2180.0000.2090.3070.1060.1160.3960.2030.4440.0660.0500.1030.1530.1270.6890.2250.1510.0000.2470.1190.3480.0000.0000.1420.3131.0000.2980.0650.0000.0300.2720.213
PVT0.0250.1520.0480.1080.0000.0810.1510.1120.1000.1500.1190.1440.0610.1480.0000.1140.0000.0000.0000.1210.0000.0000.0000.0000.0000.0000.0000.1590.2981.0000.2450.1020.0790.0880.070
Packs_year0.2890.0590.0000.2440.0000.2320.1760.3130.2190.2300.0000.1790.0000.2270.1790.4100.2600.0000.2030.3140.5510.2880.3310.1490.0000.4080.1390.2700.0650.2451.0000.8110.2720.2260.278
Smoking0.0000.2520.1140.0000.1320.1170.0000.0000.0000.0830.0000.0000.0000.0000.2960.1690.0760.0000.0000.0860.0620.0000.0000.1300.0000.1000.0420.1260.0000.1020.8111.0000.1710.0000.054
Spleno0.2260.1320.0000.0000.1870.0830.2730.0880.2740.0000.0000.1820.0000.1170.1260.0960.0130.0210.0000.0250.0000.1000.0000.0000.1090.0000.0000.5500.0300.0790.2720.1711.0000.0000.600
Symptoms0.1290.1190.1380.1430.2510.0000.1710.0000.0770.2670.1120.0790.0000.0000.0000.0000.0950.0000.0000.0000.1200.0000.0000.2160.0000.0930.1060.0000.2720.0880.2260.0000.0001.0000.129
Varices0.1280.2440.0000.0550.1960.1810.2800.0000.3550.0000.0000.1240.0440.1220.0740.1480.0000.0000.0000.0000.0000.0000.0000.0000.2170.0000.0000.6000.2130.0700.2780.0540.6000.1291.000

Missing values

2025-02-23T11:46:51.836762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-23T11:46:52.218791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderSymptomsAlcoholHBsAgHBeAgHBcAbHCVAbCirrhosisEndemicSmokingDiabetesObesityHemochroAHTCRIHIVNASHVaricesSplenoPHTPVTMetastasisHallmarkAgeGrams_dayPacks_yearPSEncephalopathyAscitesINRAFPHemoglobinMCVLeucocytesPlateletsAlbuminTotal_BilALTASTGGTALPTPCreatinineNoduleMajor_DimDir_BilIronSatFerritinClass
01010000101101000010000167137150111,539513,7106,64,9993,42,134411831507,10,713,50,552,5378561
10000001101100100010000162000111,242,610,3836,11,713,90,811287712070,5811,80,853210181
2101101010100011000010117850502120,965,88,979,88,44723,30,4586820210972,15130,1286161
3111000010110010000000117740300110,95244013,497,192793,70,41664941748,11,11215,70,21317813160
41111010101000110000000176100300110,944914,395,16,41994,10,71473061731096,91,8190,15915221
5101000010101000001110017510067,51121,5811013,491,55,4853,43,5911222423965,60,91101,453221110
61000011100100000000000149000111,4138,910,41023,2420002,352,721191831432117,30,852,62,1917112614520
7111000010110000000111016160203111,46986010,8923583,13,2791081843007,10,52291,342257060
81110000101100100011100150100321123,148,811,9107,54,9701,93,32659115636,10,5916,41,285739821
9111000010000100000100004310000111,121,811,887,851001930004,20,571452563037,10,5919,30,737111731
GenderSymptomsAlcoholHBsAgHBeAgHBcAbHCVAbCirrhosisEndemicSmokingDiabetesObesityHemochroAHTCRIHIVNASHVaricesSplenoPHTPVTMetastasisHallmarkAgeGrams_dayPacks_yearPSEncephalopathyAscitesINRAFPHemoglobinMCVLeucocytesPlateletsAlbuminTotal_BilALTASTGGTALPTPCreatinineNoduleMajor_DimDir_BilIronSatFerritinClass
194111101010000000001110116410053331,572838,4112,24,268596,592,512,8560106858550,853,71,1856,9273660
19501100001001000000001001799683221,251524,31291,17,5327084,473,481,0124532612106,70,74271,137726740
1961110011101001000010100063128462122,14523,211,588,96737,6206182,473,311,6592977124467,20,65210,21,81187,2764350
197111000010110010000000117443281111,043726,812,996,28240,673,60,8927721101967,91,01214,50,39115,66912100
1981110000100000000000011165100201111,01337,212,490,64683,8216523,073,450,710322810205757,20,7637,11,1137,9667180
19900000011001000100110000701770111,33502,611,1102,86,990846,83,182,6710212638911775,2528,81,3573,3468730
2000110000100100000000100180107133221,352353,712,295,26,7332033,673,261,0724562292316,50,7827,61,0469,323700
20111100001000000000001001808603111,5523,410,296,37,4195,762,933,22666846930471,071121,5771,2291060
202111000010010011001110117412403211,336,113861118,4101884,413,311,4529482031977,21,08530,6394,4838590
2030100000000000110000001182011111,09798,911,489,4193,2317292,713,40,8761017305797,22,13516,10,1114,651610